README added

README.md

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+# Tree structure
+
+This code is meant for an efficient calculation of kinetic Monte Carlo method
+
+## How to compile
+
+Download all files and run `python setup.py build_ext --inplace`.
+
+## How to use it
+
+Suppose you have $N$ diffusing solutes (such as vacancies or interstitial atoms). Let's say each of them has 4 possible jumps. Then you can create the tree by:
+
+```
+from tree import Tree
+import numpy as np
+
+tree = Tree()
+tree.append([[0.1, 0.2, 0.3, 0.4]
+             [0.5, 0.6, 0.7, 0.8], # jump probabilities
+	    [0, 1] # indices
+	   )
+```
+
+Then you can put a random number between 0 and 1:
+
+```
+tree.choose_event(0.5)
+tree.get_index() # This will return 1
+tree.get_jump_id() # This will return 2
+
+# Then either remove the leaf by:
+tree.remove()
+
+# Or update the values for example by:
+tree.update([0.9, 1, 1.1, 1.3])
+
+```